@inproceedings{83f1b7db02a84888a53556572b93a412,
title = "WaveSNet: Wavelet Integrated Deep Networks for Image Segmentation",
abstract = "In deep networks, the lost data details significantly degrade the performances of image segmentation. In this paper, we propose to apply Discrete Wavelet Transform (DWT) to extract the data details during feature map down-sampling, and adopt Inverse DWT (IDWT) with the extracted details during the up-sampling to recover the details. On the popular image segmentation networks, U-Net, SegNet, and DeepLabV3+, we design wavelet integrated deep networks for image segmentation (WaveSNets). Due to the effectiveness of the DWT/IDWT in processing data details, experimental results on CamVid, Pascal VOC, and Cityscapes show that our WaveSNets achieve better segmentation performances than their vanilla versions.",
keywords = "Deep network, Image segmentation, Wavelet transform",
author = "Qiufu Li and Linlin Shen",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.; 5th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2022 ; Conference date: 04-11-2022 Through 07-11-2022",
year = "2022",
doi = "10.1007/978-3-031-18916-6_27",
language = "English",
isbn = "9783031189159",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "325--337",
editor = "Shiqi Yu and Jianguo Zhang and Zhaoxiang Zhang and Tieniu Tan and Yuen, {Pong C.} and Yike Guo and Junwei Han and Jianhuang Lai",
booktitle = "Pattern Recognition and Computer Vision - 5th Chinese Conference, PRCV 2022, Proceedings",
address = "Germany",
}